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How ML-powered video surveillance could improve security

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The expanding use of surveillance cameras, whether in service of public safety, health monitoring or commercial operations, has heightened concerns about privacy. These days, it seems people’s movements will be captured on CCTV cameras regardless of where they go.

The number of surveillance systems in use has grown, with no signs of slowing down. According to the U.S. Bureau of Labor Statistics, the number of surveillance camera installations in the U.S. grew from 47 million to 85 million from 2015 to 2021, an increase of 80%. That’s roughly one camera installation for nearly every 4 people in the country. Globally, the number of surveillance cameras in use was expected to exceed a billion in 2021, according to the most recent research by IHS Markit. And the video surveillance market is expected to grow at an annual rate of more than 10% through 2026, according to Reportlinker. 

The increasing reach of these systems has heightened fears about infringements on privacy, especially concerning the use of facial recognition. In addition to the loss of privacy such as that resulting from China’s widespread use of facial recognition, studies by MIT and Stanford University, as well as other institutions, have revealed built-in biases in facial recognition systems.

Some cities in the U.S. have responded. In 2019, San Francisco banned the use of facial recognition in local agencies’ surveillance cameras, and since then, at least a dozen other U.S. cities have instituted bans of facial recognition for one use or another. But more surveillance doesn’t necessarily have to mean less privacy.

Improvements in machine learning (ML) technology can both improve the efficiency of gleaning data from surveillance camera feeds, while also going a long way toward protecting the privacy of people who appear in those feeds. A smart camera can, for example, perform processing locally, eliminating the need to transmit and store data. It also can have the intelligence to know the difference between what it should be capturing and what it should ignore. While more efficiently performing its tasks, a smart camera can also help prevent both intentional and unintentional misuse of data

How deep learning protects privacy

Along with becoming increasingly widespread, surveillance cameras have also become more powerful, with high-resolution lenses, greater local computing capacity and high-bandwidth Internet connections. In some systems, the use of machine learning and artificial intelligence (AI) have improved the ability to search the hundreds or thousands of hours of video recorded by those systems.

While making video surveillance systems more powerful and potentially intrusive, ML and AI can also be used to protect privacy. Video intelligence software based on deep learning — a subset of AI — can be trained to focus on what it should be watching and effectively look away from what it should not.

Deep learning, designed to mimic the functions of the human brain by using a neural network of three or more layers, can discover on its own how to identify and classify objects and patterns. By using tagged data to train the system, a machine can “learn” to work independently, becoming more proficient as it is exposed to more data over time. Significantly, it can do this with a small footprint that allows for embedded, localized processing that can effectively manage data privacy. 

In one example, a CCTV system equipped with deep learning software can classify people approaching a building entrance (like an office, stadium or theater), allow or deny entry, and then dispose of any captured information. By processing information locally without the need to transmit or store data, it can collect the minimum amount necessary, then “forget” about it afterward. In another example, a camera monitoring a business’ parking lot might also have a view into the window of a neighboring house. The system can prevent recording any images from that window. The software thus corrects for any complications caused by the positioning of the camera, and avoids both accidental mistakes or intentional activity involving recording images not on the business’ property.

ML makes data actionable

Along with keeping improper information out, video intelligence software also makes finding the right information in both live and archived video feeds more efficient. Monitoring or retrieving information from video recordings has often involved manual review by human eyes, which is not only time-consuming but can easily lead to oversights, mistakes and privacy violations. ML video content analysis software with deep learning can extract, classify and quickly index targeted objects — such as humans or vehicles — making video feeds significantly more searchable, actionable and quantifiable. 

The classification and indexing of objects also enable intelligent alerts when certain objects, behaviors or anomalous activity is detected. This can include count-based alerts when the number of people in a certain area exceeds a set limit, alerts triggered by object identification or, where applicable, face recognition.

Video content analysis also aggregates metadata from live or archived feeds, allowing analysts to understand trends and develop procedures for improving safety, operations and security. And by using properly implemented deep learning technology, it can do it without increasing risks to privacy.

Improving video surveillance while managing data privacy

Concerns over privacy and attempts to limit the use of facial recognition notwithstanding, the amount of video and other data being collected isn’t going to slow down. Video systems can, for example, help health officials track the number of people wearing masks or who are observing safe-distancing practices. Municipal officials can get a clear view of traffic flows and bottlenecks. Businesses can monitor people’s shopping habits. The security of public places increasingly depends on good video surveillance.

Beyond those uses, the spread of home systems with surveillance capabilities also is driving fears over lost privacy. More than 128 million cloud-connected voice assistants—such as Google Home, Amazon Echo and Facebook Portal—are in use in U.S. homes, with the ability to record and share information. And 76% of TV households report that they have smart TVs, which have raised concerns over their potential to spy on users. 

However, the way video is collected, processed and searched can achieve the goals of tighter security, better operations or improved safety without further compromising privacy. The current approach of using cloud-connected surveillance cameras with cloud-based analytics doesn’t stand up to privacy and bias concerns. But ML software with deep learning capabilities allows for localized, embedded intelligence and analytics — delivering high performance at low power — that can improve safety while managing data privacy. In the case of CCTV video surveillance systems, intelligent video technology can also be seamlessly integrated with most existing systems. 

Using deep learning technologies can also drive future improvements, empowering organizations to continually increase the sophistication of their systems through additional AI applications.

David Gamba is the vice president of Sima AI.

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FIFA 23 lets you turn off commentary pointing out how bad you are

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FIFA 23 lets you turn off commentary pointing out how bad you are
A player shouldering the ball



(Image credit: EA)

FIFA 23 might be the best game soccer game yet for terrible sports fans, as it lets you turn off commentary that criticizes your bad playing.

Now that the early access FIFA 23 release time has passed, EA Play and Xbox Game Pass Ultimate subscribers can hop into the game ahead of its full release. But as Eurogamer (opens in new tab) spotted, they’ll find a peculiar option waiting for them.

FIFA 23 includes a toggle to turn off ‘Critical Commentary’. The setting lets you silence all negative in-match comments made about your technique, so you can protect your precious ego even when you miss an open goal or commit an obvious foul. The more positive commentary won’t be affected. 

Spare your feelings

A player dribbling the ball in FIFA 23

(Image credit: EA)

The feature looks tailored toward children and new players, who don’t want to have their confidence wrecked within mere minutes of picking up the controller. But even experienced players who just so happen to be terrible at the game might benefit.

It’s not perfect, though. According to Eurogamer, the feature didn’t seem to work during a FIFA Ultimate Team Division Rivals match, with critical comments slipping through the filter. Still, who hasn’t benefited from a light grilling every now and then?

Polite commentary isn’t the only new addition in FIFA 23. It’s the first game in the series to include women’s club football teams, and fancy overhauled animations that take advantage of the PS5 and Xbox Series X|S’s new-gen hardware. EA will be hoping to end on a high, as FIFA 23 will be the last of its soccer games to release with the official FIFA licence.

If disabling critical commentary doesn’t improve your soccer skills, maybe building a squad of Marvel superheroes will. Although you might not do much better with Ted Lasso wandering the pitch.

FIFA 23 is set to fully release this Friday, September 30.

Callum is TechRadar Gaming’s News Writer. You’ll find him whipping up stories about all the latest happenings in the gaming world, as well as penning the odd feature and review. Before coming to TechRadar, he wrote freelance for various sites, including Clash, The Telegraph, and Gamesindustry.biz, and worked as a Staff Writer at Wargamer. Strategy games and RPGs are his bread and butter, but he’ll eat anything that spins a captivating narrative. He also loves tabletop games, and will happily chew your ear off about TTRPGs and board games. 

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Google Pixel 7 price leak suggests Google is totally out of touch

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Google Pixel 7 price leak suggests Google is totally out of touch
The backs of the Pixel 7 and the Pixel 7 Pro



(Image credit: Google)

We’re starting to hear more and more Google Pixel 7 leaks, with the launch of the phone just a week away, but tech fans might be getting a lot of déjà vu, with the leaks all listing near-identical specs to what we heard about the Pixel 6 a year ago.

It sounds like the new phones – a successor to the Pixel 6 Pro is also expected – could be very similar to their 2021 predecessors. And a new price leak has suggested that the phones’ costs could be the same too, as a Twitter user spotted the Pixel 7 briefly listed on Amazon (before being promptly taken down, of course).

Google pixel 7 on Amazon US. $599.99.It is still showing up in search cache but the listing gives an error if you click on it. We have the B0 number to keep track of though!#teampixel pic.twitter.com/w5Z09D28YESeptember 27, 2022

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According to these listings, the Pixel 7 will cost $599 while the Pixel 7 Pro will cost $899, both of which are identical to the Pixel 6 and Pixel 6 Pro starting prices. The leak doesn’t include any other region prices, but in the UK the current models cost £599 and £849, while in Australia they went for AU$999 and AU$1,299.

So it sounds like Google is planning on retaining the same prices for its new phones as it sold the old ones for, a move which doesn’t make much sense.


Analysis: same price, new world

Google’s choice to keep the same price points is a little curious when you consider that the specs leaks suggest these phones are virtually unchanged from their predecessors. You’re buying year-old tech for the same price as before.

Do bear in mind that the price of tech generally lowers over time, so you can readily pick up a cheaper Pixel 6 or 6 Pro right now, and after the launch of the new ones, the older models will very likely get even cheaper.

But there’s another key factor to consider in the price: $599 might be the same number in 2022 as it was in 2021, but with the changing global climate, like wars and flailing currencies and cost of living crises, it’s a very different amount of money.

Some people just won’t be willing to shell out the amount this year, that they may have been able to last year. But this speaks to a wider issue in consumer tech.

Google isn’t the only tech company to completely neglect the challenging global climate when pricing its gadgets: Samsung is still releasing super-pricey folding phones, and the iPhone 14 is, for some incomprehensible reason, even pricier than the iPhone 13 in some regions. 

Too few brands are actually catering to the tough economic times many are facing right now, with companies increasing the price of their premium offerings to counter rising costs, instead of just designing more affordable alternatives to flagships.

These high and rising prices suggest that companies are totally out of touch with their buyers, and don’t understand the economic hardship troubling many.

We’ll have to reach a breaking point sooner or later, either with brands finally clueing into the fact that they need to release cheaper phones, or with customers voting with their wallets by sticking to second-hand or refurbished devices. But until then, you can buy the best cheap phones to show that cost is important to you.

Tom’s role in the TechRadar team is to specialize in phones and tablets, but he also takes on other tech like electric scooters, smartwatches, fitness, mobile gaming and more. He is based in London, UK.

He graduated in American Literature and Creative Writing from the University of East Anglia. Prior to working in TechRadar freelanced in tech, gaming and entertainment, and also spent many years working as a mixologist. Outside of TechRadar he works in film as a screenwriter, director and producer.

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DisplayMate awards the “Best Smartphone Display” title to the iPhone 14 Pro Max

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DisplayMate awards the “Best Smartphone Display” title to the iPhone 14 Pro Max

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